Apparatus and method for neural processing

摘要

The neural computing paradigm is characterized as a dynamic and highly computationally intensive system typically consisting of input weight multiplications, product summation, neural state calculations, and complete connectivity among the neurons. Herein is described neural network architecture for a Scalable Neural Array Process (SNAP) which uses a unique interconnection and communication scheme within an array structure that provides high performance for completely connected network models such as the Hopfield model. SNAP's packaging and expansion capabilities are addressed, demonstrating SNAP's scalability to larger networks. The array processor is made up of multiple sets of orthogonal interconnections and activity generators. Each activity generator is responsive to an output signal in order to generate a neuron value. The interconnection structure also uses special adder trees which respond in a first state to generate an output signal and in a second state to communicate a neuron value back to the input of the array processor.